BPNN's Empirical Analysis of Daily Rupiah Exchange Rate Volatility Utilizing Hidden Neuron Optimization

Authors

Abstract

The exchange rate is the greatest financial market in its application. As a result, traders, investors, and other money market participants must be aware of the movement of currency exchange rate data. The fluctuation, or rise and fall, of currency exchange rates reveals the level of volatility in a country. The Backpropagation Neural Network is one of the models that can grasp the features of currency exchange rates (BPNN). BPNN is made up of three layers: input, hidden, and output, and each layer contains neurons. One of the challenges in designing a BPNN network architecture is determining the ideal number of hidden layer neurons. In this work, ten methodologies will be utilized to determine the number of hidden neurons; the ten approaches provide distinct empirical results in accordance with the goal of this study, which is to perform an empirical analysis of currency exchange rates by maximizing the number of hidden neurons. Empirical results reveal that the approach for calculating the number of hidden neurons performs well in terms of MAE and MSE. For the following seven periods, the best approach is used to forecast the Rupiah exchange rate.

References

Amran, I. M., & Ariffin, A. F. (2020). Forecasting Malaysian Exchange Rate using Artificial Neural Network. Jurnal Intelek, 15(2), 136–145. https://doi.org/10.24191/ji.v15i2.323

Asadullah, M., Ahmad, N., & Dos-Santos, M. J. P. L. (2020). Forecast Foreign Exchange Rate: The Case Study of PKR/USD. Mediterranean Journal of Social Sciences, 11(4), 129. https://doi.org/10.36941/mjss-2020-0048

Baum, E. B., & Haussler, D. (1989). What Size Net Gives Valid Generalization? Neural Computation, 1(1), 151–160. https://doi.org/10.1162/neco.1989.1.1.151

Bigus, J. P. (2009). Data Mining with Neural Networks. New York, 5(1), 1–154. http://dl.acm.org/citation.cfm?id=231007

Faris, H., Mirjalili, S., & Aljarah, I. (2019). Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics, 10(10), 2901–2920. https://doi.org/10.1007/s13042-018-00913-2

Grabusts, P., & Zorins, A. (2015). The influence of hidden neurons factor on neural network training quality assurance. Vide. Tehnologija. Resursi - Environment, Technology, Resources, 3, 76–81. https://doi.org/10.17770/etr2015vol3.213

Hill, T., O’Connor, M., & Remus, W. (1996). Neural network models for time series forecasts. Management Science, 42(7), 1082–1092. https://doi.org/10.1287/mnsc.42.7.1082

Hu, M. Y., Zhang, G. P., Jiang, C. X., & Patuwo, B. E. (1999). A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decision Sciences, 30(1), 197–216. https://doi.org/10.1111/j.1540-5915.1999.tb01606.x

Huang, W., Lai, K. K., Nakamori, Y., Wang, S., & Yu, L. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology and Decision Making, 6(1), 113–140. https://doi.org/10.1142/S021962200700237X

Hunter, D., Yu, H., Pukish, M. S., Kolbusz, J., & Wilamowski, B. M. (2012). Selection of proper neural network sizes and architectures-A comparative study. IEEE Transactions on Industrial Informatics, 8(2), 228–240. https://doi.org/10.1109/TII.2012.2187914

Madhiarasan, M., & Deepa, S. N. (2016). A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting. Applied Intelligence, 44(4), 878–893. https://doi.org/10.1007/s10489-015-0737-z

Madhiarasan, M., & Deepa, S. N. (2017). Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artificial Intelligence Review, 48(4), 449–471. https://doi.org/10.1007/s10462-016-9506-6

Markova, M. (2019). Foreign exchange rate forecasting by artificial neural networks. AIP Conference Proceedings, 2164(October). https://doi.org/10.1063/1.5130812

Memon, I. Z., Talpur, S., Narejo, S., Junejo, A. Z., & Hassan, F. (2020). Short-term prediction model for multi-currency exchange using artificial neural network. Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020, June, 102–106. https://doi.org/10.1109/ICICT50521.2020.00024

Meng, A., Ge, J., Yin, H., & Chen, S. (2016). Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Conversion and Management, 114, 75–88. https://doi.org/10.1016/j.enconman.2016.02.013

Mukhlis, I. (2011). Analisis Volatilitas Nilai Tukar Mata Uang Rupiah Terhadap Dolar. Journal of Indonesian Applied Economics, 005(02), 172–182. https://doi.org/10.21776/ub/jiae/2016/005.02.8

Panda, C., & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29(2), 227–236. https://doi.org/10.1016/j.jpolmod.2006.01.005

Pandey, T. N., Jagadev, A. K., Dehuri, S., & Cho, S. B. (2020). A novel committee machine and reviews of neural network and statistical models for currency exchange rate prediction: An experimental analysis. Journal of King Saud University - Computer and Information Sciences, 32(9), 987–999. https://doi.org/10.1016/j.jksuci.2018.02.010

Pang, X., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2020). An innovative neural network approach for stock market prediction. Journal of Supercomputing, 76(3), 2098–2118. https://doi.org/10.1007/s11227-017-2228-y

Qian, G., & Yong, H. (2013). Forecasting the Rural Per Capita Living Consumption Based on Matlab BP Neural Shanghai University of Engineering Science. 4(17), 131–137.

Sahu, K. K., Nayak, S. C., & Behera, H. S. (2020). Forecasting Currency Exchange Rate Time Series With Fireworks Algorithm-Based Higher Order Neural Network, With Special Attention To Training Data Enrichment. Computer Science, 21(4), 463–489. https://doi.org/10.7494/csci.2020.21.4.3474

SANUSI, N. A., MOOSIN, A. F., & KUSAIRI, S. (2020). Neural Network Analysis in Forecasting the Malaysian GDP. Journal of Asian Finance, Economics and Business, 7(12), 109–114. https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.109

Sheela, K. G., & Deepa, S. N. (2014). Selection of number of hidden neurons in neural networks in renewable energy systems. Journal of Scientific and Industrial Research, 73(10), 686–688.

Sheela, K. Gnana, & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013. https://doi.org/10.1155/2013/425740

Shin, N. R., Yun, D. Y., & Hwang, C. (2020). Artificial neural network algorithm comparison for exchange rate prediction. 12(3), 125–130.

Wang, J., & Hu, J. (2015). A robust combination approach for short-term wind speed forecasting and analysis - Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts usi. Energy, 93, 41–56. https://doi.org/10.1016/j.energy.2015.08.045

Wei Huang, K.K. Lai, Y. Nakamori, S. W. (1995). Forecasting foreign exchange rates with artificial neural networks. Intelligent Engineering Systems Through Artificial Neural Networks, 5(1), 771–778.

Wei, Y., Sun, S., Ma, J., Wang, S., & Lai, K. K. (2019). A decomposition clustering ensemble learning approach for forecasting foreign exchange rates. Journal of Management Science and Engineering, 4(1), 45–54. https://doi.org/10.1016/j.jmse.2019.02.001

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Published

2022-05-26

How to Cite

Saluza, I., Faradillah, & Anggraini, L. (2022). BPNN’s Empirical Analysis of Daily Rupiah Exchange Rate Volatility Utilizing Hidden Neuron Optimization. Jurnal AKSI (Akuntansi Dan Sistem Informasi), 7(1). Retrieved from http://journal.pnm.ac.id/index.php/aksi/article/view/249

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